Darshan_project_2_501
# library
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library(ggthemes)
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library(ggbeeswarm)
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## compact#Dataset
market_sales <- read.csv("Market_Sales.csv", header = TRUE)(1)Univariate Analysis with 1 numeric and 1 categorical variable = histogram
mark_hist <- ggplot(market_sales,aes(Item_Visibility,fill = Item_Type))+
geom_histogram(bins = 70)+
ylim(0,60)+
xlim(0,0.4)+
labs(y = "Number Of Items",
x = "Item Visibility",
title = "A projection of the visibility per Item",
subtitle = "By Outlet Type") +
theme_economist()
ggplotly(mark_hist)##############################################
(2)Univariate Analysis with 1 numeric variable
a <- ggplot(market_sales, aes(x = Item_Weight))+
geom_dotplot(aes(fill =Item_Weight),
fill = "Light Green",alpha =0.5,
binwidth = 1/4) +
ylim(0,4)+
labs(y = "Count",
x = "Item Weight",
title = "A projection of Item Weight") +
theme_classic()
a
##############################################
(3)Bivariate Analysis with 1 numeric and 1 categorical variable: density plot
density(market_sales$Item_Weight)##
## Call:
## density.default(x = market_sales$Item_Weight)
##
## Data: market_sales$Item_Weight (823 obs.); Bandwidth 'bw' = 1.098
##
## x y
## Min. : 1.316 Min. :0.0000259
## 1st Qu.: 7.148 1st Qu.:0.0151561
## Median :12.980 Median :0.0568710
## Mean :12.980 Mean :0.0428233
## 3rd Qu.:18.812 3rd Qu.:0.0621254
## Max. :24.644 Max. :0.0708583mark_den <- ggplot(market_sales,aes(x = Item_Weight)) +
geom_density(fill = "red",alpha =0.5) +
xlim(0,30)+
geom_vline(aes(xintercept=mean(Item_Weight)),
color="blue", linetype="dashed", size=.5)+
labs(y = "Density",
x = "Items weight",
title = "A projection of Fat wise item weight") +
facet_grid(.~market_sales$Item_Fat_Content)+
theme_excel()
ggplotly(mark_den) ##############################################
Outlate Type VS sales
ggplot(market_sales,aes(x = Outlet_Type, y = Item_Outlet_Sales)) +
geom_jitter(
aes(shape = Outlet_Type, color = "Red"),
position = position_jitter(0.2),
size = 1.3,color = "blue",alpha = 0.7)+
stat_summary(
aes(x = Outlet_Type),
fun.data="mean_sdl",
geom = "pointrange", size = 0.7) +
labs(y = "Outlate Sales",
x = "Outlate Type",
title = "A projection of Sales per Outlate")+
theme_fivethirtyeight()
##############################################
Gender VS item quantity // gender wise product pick up quantity
gender <- market_sales$Gender
quantity <- market_sales$Quantity
ggplot(market_sales, aes(y = gender ,
x = quantity,
color = gender)) +
labs(title = "Product pick up quantity by different Gender",
x = "Quantity",
y = "Gender") +
geom_boxplot(width = .2,
fill = "pink",
outlier.color = "orange",
outlier.size = 2)
theme_clean() +
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